Detection and benchmarking of somatic mutations in cancer ...€¦ · 2018-01-17 · in WES. We...

Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

Alexandre Coudray1, Anna M. Battenhouse2, Philipp Bucher3�Vishwanath R. Iyer2

1 School of Life Sciences, Swiss Federal Institute of Technology (EPFL), CH-1015, Lausanne, Switzerland 2 Center for Systems & Synthetic Biology and Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX USA 3 Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), CH-1015, Lausanne, Switzerland

Corresponding Author: Vishwanath R. Iyer 100 East 24th St. Stop A5000, Austin, TX 78712-1639, USA Email address: [email protected]

Keywords: Somatic mutations; RNA-seq; cancer; variants

.CC-BY-NC-ND 4.0 International licenseacertified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under

The copyright holder for this preprint (which was notthis version posted January 17, 2018. ; https://doi.org/10.1101/249219doi: bioRxiv preprint

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2

ABSTRACT

To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often

used for its reliability and relative low cost. RNA-seq, while generally used to measure gene

expression, can potentially also be used for identification of somatic mutations. However there has

been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here,

we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme

(GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR

aligner 2-pass procedure jointly with MuTect2 from GATK to detect somatic variants. Variants

identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP

databases, and also compared to somatic variants identified by exome sequencing. We also

estimated the putative functional impact of coding variants in the most frequently mutated genes

in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of

GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed

the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways,

and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq

calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer

genomes can thus broaden the scope of discoveries and lend additional support to somatic variants

identified by exome sequencing alone.



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INTRODUCTION

Cancer is among the leading causes of death worldwide, with 8.7 million deaths in 2015 (Global

Burden of Disease Cancer Collaboration 2017). As a genetic disease, cancers are driven in part by

the accumulation of somatic mutations, which incidentally, also offer targets for new precision

therapies directed against tumor-causing mutations. Cancer cells typically accumulate somatic

alterations that impact specific pathways implicated in cell growth, survival, angiogenesis, motility

and other hallmarks of cancer (Hanahan & Weinberg 2011). Advances in next-generation

sequencing technologies have allowed increasingly fast, accurate and cost-efficient analysis of

DNA and RNA samples, which has driven the identification of key cancer-driving mutations

(Raphael et al. 2014). These findings are beginning to pave the way for new targeted therapies in

many cancers, but significant challenges remain (Paez et al. 2004; Taylor et al. 2012).

The actual cancer-driving mutations need to be differentiated from somatic passenger mutations

caused by impaired DNA repair mechanisms (Shen 2011), inherited or de novo germline mutations

and neutral polymorphisms, and artefacts that can arise from sequencing errors, PCR or

misalignment. Moreover, the complex structure of tumors increases the complexity of the analysis,

as tumors are typically heterogeneous, containing normal cells as well as distinct clonal lineages

of tumor cells. Somatic alterations typically range from substitution mutations and small

insertions/deletions (indels) to chromosome rearrangements and copy number variations (Rhee et

al. 2017).

To detect mutations in a tumor sample, whole exome sequencing (WES) has generally been

favored over whole genome sequencing (WGS) for its relatively low cost, although dropping costs

of WGS encourage its use for somatic mutation identification (Alioto et al. 2015; Puente et al.

2011). Whole-transcriptome (RNA-seq) data has typically been used to measure gene expression



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and identify transcript and splicing isoforms. Nevertheless, it is possible to identify genomic

variants from RNA-seq (Piskol et al. 2013). Previous studies examining the use of RNA-seq for

somatic mutation detection have focused on the characteristics of mutational changes seen in

RNA-seq versus WES, but these studies have been limited with regard to cancer type, and there

has been little systematic evaluation of the biological novelty and significance of tumor somatic

variants detected by RNA-seq (O'Brien et al. 2015).

Here, we assessed the utility of RNA-seq for somatic mutation detection in glioblastoma

multiforme (GBM), the most common and deadliest form of primary brain cancer. GBM shows a

median overall survival of only 14-15 months (Stupp et al. 2009). Standard of care for GBM has

not changed for many years, and emerging new targeted therapies (mostly targeting angiogenesis-

related pathways) unfortunately encounter problems of drug resistance (Stavrovskaya et al. 2016),

making the discovery of new target genes of great importance. We focused on the use of the STAR

aligner (Dobin et al. 2013) which is fast and is transcript-aware, and therefore has the potential to

give additional information about mutations in cancer-activated transcripts that might be missing

in WES.

We developed a new pipeline to detect somatic mutations in RNA-seq data, combining RNA-seq

alignment using a STAR 2-pass procedure with somatic mutation detection using MuTect2 for

variant calling. Variants from RNA-seq and WES were compared, first, on a pair of RNA-

seq/WES from a GBM tumor that had already been analyzed in our laboratory and then on a set

of 9 pairs of RNA-seq and WES data from GBM tumors analyzed by the Cancer Genome Atlas

(TCGA) (Brennan et al. 2013). We compared and evaluated RNA-seq and WES mutations in four

steps. First, we estimated the proportion of germline/somatic mutations by comparison of

identified variants to the dbSNP database (Kitts et al. 2013) which catalogs known germline



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variants, and the Catalogue Of Somatic Mutations in Cancer or COSMIC database (Forbes et al.

2015). The use of these databases allowed to evaluate whether a variant was a germline (included

in dbSNP but not in COSMIC) or a somatic mutation (included in COSMIC but not in dbSNP).

Second, somatic mutations detected in RNA-seq-only data were consolidated to highlight

mutations present in multiple tumor samples. Third, their functional impact on proteins was

evaluated by using two scoring systems: SIFT and FATHMM with cancer-weights

(FATHMMcw). Finally, we focused on mutations affecting a set of 29 genes already shown to be

implicated in GBM by a previous TCGA study (Cancer Genome Atlas Research Network 2008).

Mutations falling into coding regions of these 29 genes and showing high likelihood of altered

protein function were assumed to be the best GBM-related mutations and potential cancer-drivers.

Our analysis showed that RNA-seq is able to detect novel, GBM-related somatic mutations and

can thus complement exome and whole-genome sequencing in identifying somatic mutations in

tumor genomes.

MATERIALS AND METHODS

Sample preparation, data origin and databases used for the analysis

We generated paired RNA-seq and WES data from one GBM tumor (SD01) collected at St.

David’s Medical Center (Austin, TX) after informed consent, in a study approved by the

Institutional Review Boards of St. David's Medical Center and of the University of Texas at Austin.

For WES and RNA-seq, we used the exome capture kit NimbleGen SeqCap EZ (Roche) and the

NEBNext small RNA kit (NEB) respectively. Sequencing was carried out at the NGS Core Facility

of the MD Anderson Cancer Center Science Park on an Illumina HiSeq 2500. For GBM data from

TCGA, BAM files resulting from alignment were downloaded from the Genomic Data Commons

data portal and used directly in the subsequent analysis pipeline since they were already aligned



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with STAR. To evaluate variants, two databases were used: dbSNP (Kitts et al. 2013) with the

b1.47 build on the GRCh38 reference, released in April 2016 (37 × 106 variants), and the COSMIC

database v78 (Forbes et al. 2015), which contains 3.3 × 106 known somatic variants. We carried

out all analyses using the GRCh38 primary assembly reference acquired from GENCODE

(Harrow et al. 2012). ANNOVAR (v.2016Feb01) (Wang et al. 2010) was used to annotate variants

relative to RefSeq annotations (release 73) (O'Leary et al. 2016).

A pipeline to detect variants from RNA-seq data with STAR 2-pass and GATK MuTect2

and distinguish GBM-related mutations

The general pipeline used is shown in Fig. 1, with slight differences between samples (SD01 and

TCGA) or techniques (RNA-seq and WES) as depicted in Fig. 1B. The workflow was adapted

from GATK best practices for variant calling (Van der Auwera 2014; Van der Auwera et al. 2013)

but using MuTect2 for variant calling. The process first involved trimming the adapters with

cutadapt (v1.10) (Martin 2011) from fastq files, removing sequences that were shorter than 36

bases after trimming, and removing rRNA and tRNA sequences by aligning with BWA (v0.7.12-

r1039) (Li & Durbin 2009) to a reference built with known rRNA/tRNA. Filtered reads were then

aligned with STAR aligner (v2.4.2a) using a 2-pass procedure (Dobin & Gingeras 2015). Before

variant calling, aligned reads in BAM format were sorted, duplicate reads were flagged

(MarkDuplicates, Picard v2.5.0), the base scores recalibrated (BaseRecalibrator, GATK v3.6) and

RNA-seq reads were split into exons (SplitNCigarReads, GATK v3.6). Variant calling was done

with MuTect2 in tumor versus normal mode as described below. Variants recovered in VCF files

were then separated into RNA-seq-only, Intersection and WES-only. ANNOVAR (v.2016Feb01)

(Wang et al. 2010) was used to annotate variants relative to RefSeq annotations (release 73)

(O'Leary et al. 2016). SIFT score/prediction (v2.3) (Ng & Henikoff 2003), and FATHMM



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score/prediction with cancer weights (v2.3) (Shihab et al. 2013a; Shihab et al. 2013b) were used

to evaluate the functional impact of non-synonymous SNVs and frameshift indels. Finally, a set

of 29 genes known to be related to GBM (Cancer Genome Atlas Research Network 2008) was

used to evaluate GBM-related mutations in specific pathways.

Variant Calling Using MuTect2 from Genome Analysis ToolKit (GATK)

MuTect2 infers genotypes with two log-odd ratios (Cibulskis et al. 2013) which score the

confidence that a mutation is present in the tumor sample (TLOD score) and is absent from the

matched-normal sample (NLOD score). The thresholds used by MuTect2 to consider a variant as

being real and somatic (leading to the annotation “PASS”) are by default TLOD > 6.3 and NLOD

> 2.2. For dbSNP variants, a higher NLOD threshold of 5.5 is used, except if the variant is also

present in the COSMIC database.

Building a Panel of Normals (PoN) for variant calling with MuTect2

The creation of a PoN is an optional step that improves variant calling by filtering out method-

specific artefacts, by doing variant calling (MuTect2) on a set of normal samples (Fig. 1A). The

samples for the PoN should ideally be obtained through protocols and data processing steps closely

matched to the tumor sample. For this reason, two PoN were built, one with RNA-seq data from

normal samples and another with WES data from normal samples. Then, variants identified by

MuTect2 in at least two normal samples were compiled together into one PoN VCF file. Although

using 30 normal samples is recommended by GATK, we used only 12 normal samples as they

were matched to the 12 GBM tumor samples from TCGA.



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MuTect2 filters

Based on the TLOD score, MuTect2 will reject a variant when a specific TLOD > 6.3 threshold is

not reached, suggesting insufficient evidence of its presence in the tumor sample (t_lod_fstar

filter). homologous_mapping_event is a filter that detects homologous sequences and filters out

variants falling into sequences that have 3 or more events observed in the tumor. clustered_events

is a filter for clustered artifacts. str_contraction filters out variants from short tandem repeat

regions. alt_allele_in_normal filters out variants if enough evidence is shown of its presence in

the normal sample (NLOD threshold > 2.0). multi_event_alt_allele_in_normal filters out a variant

when multiple events are detected at the same position in the matched-normal sample.

germline_risk filters out variants that show sufficient evidence of being germline based on dbSNP,

COSMIC and the matched-normal sample (NLOD value). panel_of_normals is filters out variants

present in at least two samples of the panel of normals.

RefSeq annotations with ANNOVAR

ANNOVAR (v.2016Feb01) (Wang et al. 2010) was used to annotate the variants in the VCF file

with RefSeq Genes annotations (release 73 with reference GRCh38) (O'Leary et al. 2016) and

SIFT scores/predictions (v2.3) (Ng & Henikoff 2003). RefSeq gives the closest gene name, or the

two closest genes whenever a variant falls within intergenic regions. RefSeq also gives information

about the type of mutation and the eventual amino acid change, whenever a variant falls in a coding

region. For effects on alternative splicing, RefSeq gives a list of all possible transcripts.

Scoring non-synonymous SNVs and indels with SIFT score

One way to assess the functional impact of an amino-acid (AA) change is to use SIFT (Ng &

Henikoff 2003), which uses homologous sequence comparison. SIFT (v2.3) gives a score based

on the frequency at which an AA appears at a specific location in functionally related protein



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sequences. The AA change is given a predicted score: Tolerated (p > 0.05) or Deleterious (p <

0.05). Low scores typically occur in highly conserved regions that tend to be intolerant to most

substitutions. On the contrary, unconserved regions tend to be more tolerant to AA changes. SIFT

indel has been developed for scoring frameshifting indels (Hu & Ng 2013), which relies on a

different algorithm based on a machine learning model. It gives a prediction of damaging or neutral

along with a confidence score.

Scoring non-synonymous SNVs and indels with FATHMM cancer-weighted scores

Functional Analysis through Hidden Markov Models (FATHMM v2.3) also uses homologous

protein sequences to find the probability of an amino acid substitution at a given position. The

algorithm relies on Hidden Markov models to compute probabilities, its final scores being a ratio

between the probability of the wild-type and the mutant AA. The version used here (Shihab et al.

2013b) also incorporates cancer weights (FATHMMcw), the frequency of cancer-associated

variants from the CanProVar database and wild type weights, the frequency of neutral

polymorphisms from UniRef database falling in the same protein region as the variants. The final

score is an indication whether an AA substitution is deleterious and associated with cancer

(prediction CANCER given for score < -0.75) or neutral (prediction PASSENGER given for score

> -0.75). FATHMM for indels (Shihab et al. 2015) works on indels shorter than 20 bp and emits a

prediction (pathogenic or neutral) together with a confidence score (expressed in %).

Criterion to build a set of 29 genes previously shown to be altered in GBM

A set of 29 genes that were shown to be the most frequently mutated genes in GBM by a TCGA

study on 91 GBM samples (Cancer Genome Atlas Research Network 2008) was used to look for

somatic mutations in GBM-related pathways. Genes selected to be part of the set were ARF,

BRCA2, CBL, CDK4, CDKN2B, CDKN2C, EGFR, EP300, ERBB2, ERBB3, FGFR2, IRS1,



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MDM2, MDM4, MET, MSH6, NF1, P16, PDGFRB, PIK3C2B, PIK3C2G, PIK3CA, PIK3R1,

PRKCZ, PTEN, RB1, SPRY2, TP53 and TSC2. These genes were shown to bear mutations in at

least 2% of samples, the most altered being ARF (49%), EGFR (45%), PTEN (36%) and TP53

(35%). The “Best GBM-related mutation” (Table 1) is indicated when a mutation was included in

this set of 29 genes, part of COSMIC database but not in dbSNP, resulted in an AA change and

retained based on both SIFT and FATHMM scores as being functionally deleterious for protein

function.

RESULTS

Read counts and variant features highlight differences between RNA-seq and WES variants

in TCGA samples

In the majority of samples, RNA-seq showed fewer uniquely mapped reads than WES (Fig. 2A

and Supplementary Figure 1). Secondary alignments and unmapped reads were generally higher

in the RNA-seq data, which could be due in part to unmapped splice junction reads and mismatches

in RNA-seq due to RNA editing. Adenosine to inosine (A-to-I) is the most common form of RNA

editing in humans, leading mainly to A>G and T>C base substitutions (Picardi et al. 2015), which

were clearly enriched in RNA-seq compared to WES data (Fig. 2B). RNA editing site databases

like DARNED (Kiran et al. 2013), RADAR (Ramaswami & Li 2014) or Inosinome Atlas (Picardi

et al. 2015) could potentially be used to filter out such variants (Piskol et al. 2013).

The proportion of variants filtered by the different MuTect2 filters are shown in Fig. 2C. MuTect2

generates two log-odd ratios, TLOD and NLOD, which can be used to infer the somatic origin of

a variant (Materials and Methods). RNA-seq variants showed lower TLOD scores and slightly

higher NLOD scores than WES variants. Low read counts or poor base qualities supporting the

altered allele in tumor can lead to low TLOD values. Fewer RNA-seq variants met the TLOD



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threshold (Fig. 2C, TLODfstar). Interestingly, TLOD scores of COSMIC variants were higher than

non-COSMIC variants (Supplementary Figure 2), suggesting that TLOD reflects the higher true

positive rate. On the other hand, variants that also occur in the matched-normal samples could be

filtered by the AltAlleleInNormal MuTect2 filter based on NLOD values. RNA-seq data from

TCGA samples showed particularly low numbers of variants excluded by this filter (Fig. 2C),

which could be due to coverage differences between tumor RNA-seq and matched-normal (the

latter being WES data). Thus, distinct variant features given as an output by MuTect2 could be

used to build a variant filtering model (Ding et al. 2012).

Variants accepted as true and somatic (PASS) by MuTect2 were higher in RNA-seq than WES for

all TCGA samples (Fig. 2D). The overlap between RNA-seq and WES was small in all samples,

but interestingly, the overlap increased with increasing significance of the variants. An average of

only 6.60% of WES variants retained by MuTect2 (PASS) were also present in RNA-seq, while

15.9% of WES variants from coding regions and 17.2% of functional mutations were common to

RNA-seq (Fig. 2D). Coverage differences between RNA-seq and WES could partially explain the

phenomenon. A previous study indeed found that ~71% of RNA-seq variants fell outside the WES

capture boundaries (O'Brien et al. 2015). Moreover, they showed that a high proportion of RNA-

seq-only variants were missed by WES because of their low allele fraction (AF).

The RNA-seq/WES intersection was enriched in variants from coding regions (89.7% of coding

variants), which was probably induced by a higher coverage overlap. RNA-seq data also showed

an unexpected level of intronic/intergenic variants. Intronic mRNA reads could partly come from

unspliced RNA (pre-mRNA). A previous study has indeed detected many intronic mRNA variants,

which could come from inefficient splicing in cancer (Sowalsky et al. 2015). On the other hand,



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intergenic RNA variants could come from unannotated genes, non-coding RNA, retrotransposons,

splicing errors (Pickrell et al. 2010) and sequencing/mapping errors.

Allele fraction and coverage are useful features to further classify variants

In theory, heterozygous mutations would show an allele fraction (AF) around 0.5. However,

somatic mutations from cancer cells are expected to appear at lower frequencies, as tumor samples

are heterogeneous and not pure clones. Moreover, copy number variation (CNVs) can lead to

gain/loss of chromosomes and/or duplications of genes (Yin et al. 2009). RNA-seq-only variants

showed a surprising AF distribution in that 38.2% showed an AF > 0.95 (Fig. 3A,B) versus only

0.50% of WES-only variants. These high AF RNA-seq-only variants showed low coverage, and

the majority of them occurred in intronic/intergenic regions (85.6% of RNA-seq-only variants with

AF > 0.95). Conversely, we also found a high number of RNA-seq-only variants showing AF <

0.05 (36.3% of RNA-seq variants representing 4267 variants in nine TCGA samples). In

comparison, WES-only data showed only 502 variants (22.8%) with AF < 0.05. Low AF RNA-

seq-only variants mainly originated from coding regions (81.1% of RNA-seq-only variants with

AF < 0.05) and often showed high coverage, which distinguished them from WES-only variants

(Fig. 3A). This region of high coverage/low AF is of particular interest as it is likely to contain

true somatic mutations that are missed in WES data.

COSMIC/dbSNP overlap can be used as an indicator of the somatic/germline content in

TCGA samples

For each of the three classes of variants – WES-only, Intersection and RNA-seq-only, we

examined the proportion in different genomic regions (Fig. 3C), potential for affecting protein

function (Fig. 3D) and representation in dbSNP and COSMIC databases (Fig. 3E). The proportion

of variants included in the dbSNP database is potentially an indicator of germline content among



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identified variants, while the overlap with the COSMIC database can serve as an indicator of

somatic mutations (Fig. 3E). However, it must be noted that with the increasing coverage in dbSNP

of variants from ever-increasing numbers of human genomes, inclusion in dbSNP cannot always

rule out a somatic variant (Nadarajah et al. 2016). Coding variants identified by both RNA-seq &

WES (Intersection) showed a particularly high proportion (87.7%) included in COSMIC but not

in dbSNP (COSMIC-only), which may be considered the most likely candidates for somatic

mutations. A high proportion of WES-only coding variants (39.5%) and a low proportion of RNA-

seq-only coding variants (3.0%) were likewise found in COSMIC-only but although the

proportions were very different, both WES-only and RNA-seq-only variants contained the same

order-of-magnitude COSMIC-only variants (Fig 3E). Thus, RNA-seq-only identified 138

COSMIC-only variants from coding regions that were therefore missed by WES-only. Because

COSMIC contains variants discovered mainly by WES, it is possible that many of the RNA-seq-

only variants unknown to both COSMIC and dbSNP, representing 96.4% of RNA-seq-only

variants from coding regions (4402 variants in 9 samples) could include many bonafide cancer

somatic mutations. We therefore explored this possibility further.

Genes showing somatic mutations in multiple TCGA samples only in RNA-seq data

There were 63 genes with RNA-seq-only variants that were mutated in 5 or more tumors, and

many genes from this group have been implicated in cancer (Fig. 4). For example, a set of three

complement related genes – complement C3, α-2 macroglobulin (A2M) and the complement lysis

inhibitor SP-40/clusterin (CLU) – that have been implicated in various cancers including gliomas

(Reis et al. 2018; Saratsis et al. 2014; Shinoura et al. 1994; Suman et al. 2016) were present in this

group, and interestingly, these three proteins have been recently shown to form a network of related

biomarkers in B-ALL (Cavalcante Mde et al. 2016). One tumor contained a cluster of highly



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mutated genes (Fig. 4, bottom left), including SPARC and FLNA, which are associated with cell-

matrix interactions and cell motility (Neuzillet et al. 2013; Xu et al. 2010), and thus possibly

involved in metastasis. On the other hand, MAGED1 was linked with cell-death mechanisms

(Mouri et al. 2013), which are often disrupted in cancer. One frameshift insertion was detected in

the ARF1 gene located at the exact same position (G14fs) in all nine samples. This was a COSMIC-

only variant with plausible AF and coverage. Despite high coverages in WES at the variant

position, the insertion was never present in WES data, and since insertions/deletions have been

shown to be more prone to artefacts (Kroigard et al. 2016), it was not retained in Table 1 and 2

(see below). Note that the mutational landscape given here is distinct from the one obtained by a

TCGA study on WES data (Brennan et al. 2013), which is not surprising as RNA-seq-only data is

likely interrogating other regions of the genome relative to WES.

Analysis of somatic mutations found by RNA-seq without a corresponding matched normal

sample

The SD01 GBM tumor sample had no corresponding matched normal to enable reliable distinction

of somatic mutations from germline variants, so it presented unusual challenges. However, it is

worthwhile to consider such samples because often, RNA-seq data may be available from a tumor

without a corresponding matched normal sample. The total number of variant called in SD01 was

much higher than the average TCGA sample (by 10.5-fold for RNA-seq and 17.7-fold for WES).

SD01 had a similar number of aligned reads as the TCGA samples for both RNA-seq and WES,

so the higher number of somatic variants could be in part due to the absence of matched-normal,

the small panel of normals used and/or by a higher underlying mutation rate in this specific tumor.

MuTect2 variant calling was carried out in tumor-only mode and only relied on TLOD values

without distinction between somatic and germline variants. Many dbSNP variants were indeed



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observed (Supplementary Figure 3). The distribution of SD01 variants by chromosome showed a

remarkably high number of variants on Chromosome 7 (Supplementary Figure 4), which could

reflect amplification of Chromosome 7, a common feature in GBM (Cancer Genome Atlas

Research Network 2008). SD01 also showed a higher density of transition variants (T>C, C>T,

A>G and G>A), which tend to be less deleterious, as expected for germline variants (Campbell &

Eichler 2013). Nevertheless, SD01 RNA-seq-only variants included several interesting candidate

somatic mutations. One of these RNA-seq-only mutations was EGFR-A702S, found in COSMIC

but not in dbSNP, and retained by both SIFT and FATHMM scores (see below). Two other

frameshift insertions were also found by RNA-seq-only data in EGFR (S229fs and W477fs), with

COSMIC variants found at the same amino-acid coordinates (Table 2). Moreover, the intersection

between RNA-seq and WES data in SD01 showed other interesting candidates, such as a point

mutation in EGFR (A289V - retained by both SIFT/FATHMM, and present in COSMIC but not

in dbSNP).

Analyzing the functional impact of somatic mutations on protein function in relation to

cancer and GBM pathways

We used the algorithms FATHMM and SIFT to evaluate the potential impact of somatic variants

on protein function in cancer pathways (Materials and Methods). The FATHMM and SIFT score

distributions showed a significant difference only for FATHMM scores between the Intersection

and WES-only (Fig. 5). Many RNA-seq-only variants scored below both FATHMM and SIFT

thresholds, indicating they could be potential functional mutations. The overall proportion of

variants retained by FATHMM and SIFT was higher for Intersection variants (11.8%, Fig. 5D),

and slightly higher in RNA-seq-only than WES-only. Mutations present among a set of 29 hand-

curated GBM-related genes were designated as the “best GBM-related mutations” (Table 1), and



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comprised 11 mutations. RNA-seq-only detected 3 of these 11 mutations, while WES-only found

2 and the Intersection between RNA-seq and WES found 6 of the 11 GBM mutations. These three

RNA-seq-only mutations (EGFR-A702S, TP53-I254S and TSC2-V296fs) are thus cancer-driver

candidates found only by RNA-seq and should therefore motivate the use of RNA-seq as they were

missed by WES. Taken together, our results suggest that the intersection between RNA-seq data

and WES yielded the highest quality GBM-related mutations in TCGA samples, for 3 reasons.

First, variants from RNA-seq/WES intersection showed 90.5% of COSMIC-only variants (Fig.

3E), an average much higher than WES-only or RNA-seq-only data. Second, coding variants from

the intersection also showed more evidence of functional alteration through their SIFT and

FATHMM scores (Fig. 5). Third, 6/11 of the “best GBM-related mutations” were identified in the

intersection (Table 1), even though it was the smallest group in term of variant number. Thus

combining RNA-seq and WES greatly improves the confidence in certain variants discovered by

WES, particularly in highly expressed genes.

New somatic/GBM-related mutations evaluation from unknown variants

Another group of findings are shown in Table 2 as being potentially undiscovered variants, as they

were neither in COSMIC nor in dbSNP, but affected one of the 29 GBM-related genes and retained

either by SIFT or FATHMM scores. These mutations are therefore the best candidates for being

new discoveries as they implicate known GBM-related pathways. RNA-seq-only data allowed the

discovery of 8/9 potentially new mutations, against only one new variant in WES-only data, which

suggests that variant calling from RNA-seq has considerable potential to generate new discoveries,

including in already well known pathways. For example, an RNA-seq-only variant, EGFR-L718R,

showed 22 variant reads out of a total of 4792 (AF 4.5 x 10-8). WES showed 101 reads at the same

position, giving a probability of only 0.39 of at least one variant read occurring in the WES data



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(based on binomial probability). Independent verification by Sanger sequencing or deeper NGS

sequencing in additional samples would be required to confirm these candidates (Zuryn & Jarriault

2013). Interestingly, COSMIC has cataloged a different variant, L718M at the same position

(Table 2).

DISCUSSION

Although WES has been the mainstay of somatic mutation identification in cancer genomes, our

study suggests that variant calling from RNA-seq offers a valuable complement. RNA-seq

revealed new variants that were clearly associated with GBM biology, were found at the same

positions as previously known variants, and yet were missed by WES. A major reason for the

ability of RNA-seq to identify new somatic variants likely comes from the higher sequencing

coverage of strongly expressed genes. Oncogenes in cancers, such as EGFR in GBM, are likely to

be highly expressed, and RNA-seq naturally provides better coverage of such genes than WES,

and hence higher statistical confidence to detect variants. Additionally, even when tumor cells

expressing active oncogenes comprise only a subset of the tumor, RNA-seq reads can capture this

overrepresentation when RNA is isolated from the bulk tumor, whereas DNA used for WES

cannot. In this regard, RNA-seq is likely to be advantageous even over whole-genome sequencing,

where it is harder to achieve the same depth of coverage over all genes as WES.

The RNA-seq variants we identified in our analysis did not appear to have significantly lower

quality than WES variants, although we saw a high number of variants with AF > 0.95 and low

coverage in RNA-seq data. Based on MuTect2 output, RNA-seq detected more somatic mutations

than WES in the TCGA samples. However, some RNA-seq variants could be considered

questionable, since RNA-seq data has been shown to be more prone to false positive calls (Cirulli

et al. 2010), in part due to errors during the RNA to cDNA conversion, mapping mismatches (Bass



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18

et al., 2012) or RNA editing processes (Danecek et al. 2012). Indels are also a source of possible

artefacts (Kroigard et al. 2016) even though the local de novo assembly done by MuTect2 should

reduce this artefact. Comparison of variants with known somatic mutations from the COSMIC

database showed that WES-only data contained more COSMIC variants than RNA-seq-only in

TCGA samples (323 vs 138, Fig. 3E). However, this representation is likely to be skewed by the

fact that COSMIC variants were primarily discovered by WES. Variants in coding regions were

represented in the same proportions in RNA-seq and WES (see Fig. 3C) and overrepresented in

the intersection, suggesting that RNA-seq and WES coverage have a higher overlap in coding

regions, and making it possible to compare mutations found in both datasets within coding regions.

We focused on variants causing an amino-acid change, for which functional impact could be

estimated with the scoring systems SIFT and FATHMM. To assess the identification of potential

cancer-drivers that were specific to GBM, we evaluated the recovery of variants in 29 genes within

specific pathways previously shown to be altered in GBM by a TCGA study (Cancer Genome

Atlas Research Network 2008). By this measure, RNA-seq-only data detected 3 out of 11 possible

variants while WES-only detected 2 out of 11, even though COSMIC variants have been primarily

discovered through WES. The intersection recovered 6 out of these 11 variants (see Table 1).

Strikingly, RNA-seq-only data outperformed WES-only in discovering new mutations falling into

these 29 GBM-related genes (8/9 findings). RNA-seq-only is thus able to not only detect already

known mutations, but detect potentially new mutations falling into known GBM-related pathways,

despite the high sequencing depth of WES. In sum RNA-seq was able to find 9 of 11 key known

mutations and 8 new discoveries, justifying its use for variant discovery in cancer. RNA-seq data

had the potential to better detect variants showing very low allelic fraction (Cirulli et al. 2010),

when more reads were available in highly expressed genes. Analysis on the coverage indeed



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19

showed numerous variants showing low AF and high coverage and therefore likely to be missed

by WES alone. Additionally, a previous study has shown that RNA-seq-only variants tend to be

missed by WES mainly because they fall outside WES capture kit boundaries (~71% of RNA-seq-

only variants vs WES), and tend to be located in highly expressed genes, which are more likely to

be related to cancer than unexpressed genes, the ones falling into WES-only data (Cirulli et al.

2010).

Several ways of improving the detection of cancer-related mutations using RNA-seq are possible.

First, it may be possible to optimize the pipeline by reducing artefacts and germline content. A

recent study developed a pipeline for analysis of variants in RNA-seq data (Piskol et al. 2013).

They used an indel realignment step and called variants in a more permissive way for RNA-seq

but at the same time requiring better base quality scores. After variant calling, they filtered out

known RNA editing sites using the RADAR database (Ramaswami & Li 2014). Second, a variant

filtering step using a machine-learning approach could be used to train a model with MuTect2

output features specifically for RNA-seq data (Spinella et al. 2016). Third, RNA-seq read

generators such as BEERS (Grant et al. 2011) or Flux simulator (Griebel et al. 2012) could be used

to optimize the pipeline in a way that would allow us to fine-tune the sensitivity/specificity. Our

work suggests that since the majority of studies on cancer-driving mutations used WES-only, they

are likely to have missed some key driver mutations that might be found using complementary

RNA-seq datasets from the same tumors.

ACKNOWLEDGMENTS

We thank M. Shpak, M. Cowperthwaite and A. W. Hall for tumor specimens and data. We thank

the Next Generation Sequencing Core Facility at the University of Texas MD Anderson Cancer

Center Science Park, which was supported by CPRIT Core Facility Support Grant RP120348, for



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20

sequencing, and the Texas Advanced Computing Center (TACC) and the Biomedical Research

Computing Facility (BRCF) at The University of Texas at Austin for HPC resources and

computational support. This work is based in part based upon data generated by The Cancer

Genome Atlas managed by the NCI and NHGRI for which we are grateful. This work was funded

in part by a grant from the Cancer Prevention Research Institute of Texas (RP120194) to V. R. I.

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TABLES

Table 1. “Best GBM-related mutations” from coding regions of SD01 and TCGA samples.

All variants shown were included in COSMIC and in a set of 29 GBM-related genes but not

dbSNP. All variants are deleterious based on scoring by SIFT and FATHMM with cancer weights.

For SIFTindel and FATHMM indels, the score is given as a confidence score of the prediction.

AF (Allele Fraction, tumor) shows the proportion of altered reads in tumor samples, with Coverage

(tumor) being the total number of reads at the variant position.

Gene Sample AA change

FATHMM score

SIFT score AF (Tumor)

Coverage (Tumor)

EGFR SD01 RNA-seq only

A702S -0.97 (CANCER)

0.01 (Del) 0.015 852

EGFR SD01 Intersection A289V -1.04 (CANCER)

0.002 (Del) 0.072 125

EGFR GBM01 Intersection

G63R -1.93 (CANCER)

0.0 (Del) 0.175 296

TP53 GBM01 Intersection

G105R -10.02 (CANCER)

0.0 (Del) 0.44 50

TP53 GBM02 RNA-seq only

I254S -9.48 (CANCER)

0.0 (Del) 0.949 390

TSC2 GBM02 RNA-seq only

V296fs 71% (pathogenic)

85.8% (Dam)

0.137 55

PTEN GBM02 Intersection

D107Y -3.06 (CANCER)

0.0 (Del) 0.69 92


R173H -6.42 (CANCER)

0.0 (Del) 0.331 173


D326fs 88% (pathogenic)

85.8% (Dam)

0.393 146

PTEN GBM07 WXS only R130Q -5.84 (CANCER)

0.0 (Del) 0.713 190

NF1 GBM10 WXS only C622F -0.83 (CANCER)

0.01 (Del) 0.403 389



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Table 2. Variants unknown by both COSMIC and dbSNP and candidates to be new GBM-

related functional somatic mutations.

Variants included in the set of 29 GBM-related genes and not included in COSMIC or dbSNP are

shown, although COSMIC contained alternative variants at the same positions for four mutations

that were found by RNA-seq-only. For SIFTindel and FATHMM indels, the score is given as a

confidence score. AF (tumor) shows the proportion of altered reads in tumor samples, with

Coverage (tumor) being the total number of reads at the variant position. Allele Fraction and

Coverage was used to further exclude potential artifacts, which are not listed here.

Gene Sample AA change

FATHMM score SIFT score

COSMIC AF (Tumor)

Coverage (Tumor)

EGFR SD01 RNA-seq-only

S229fs 93% (pathogenic) 85.8% (Dam)

no (S229C)

0.045 169

EGFR SD01 RNA-seq-only

W477fs 51% (neutral) 85.8% (Dam)

no (W477*)

0.046 447

PIK3C2 SD01 WES-only

I255N -3.49 (CANCER) 0 (Del) no 0.433 64

CDKN2C GBM02 RNA-seq-only

V130A -0.21 (PASSENGER)

0.03 (Del)

no 0.027 470

PDGFRB GBM02 RNA-seq-only

V840A -2.34 (CANCER) 0.23 (Tol)

no 0.021 262

RB1 GBM03 RNA-seq-only

L872fs 77% (pathogenic) 85.8% (Dam)

no 0.035 355

EGFR GBM05 RNA-seq-only

M600T -1.69 (CANCER) 0.38 (Tol)

no (M600V)

8.1E-03 6240

EGFR GBM05 RNA-seq-only

L718R -2.85 (CANCER) 0 (Del) no (L718M)

4.5E-03 4792

PDGFRB GBM06 RNA-seq-only

Q1075R -1.25 (CANCER) 0.52 (Tol)

no 0.058 90



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FIGURE LEGENDS

Figure 1. Pipeline used to detect RNA-seq variants.

(A) Principal steps in the pipeline used to identify and annotate somatic mutations. Mutation

calling was done for each paired tumor sample/matched-normal. A RNA-seq-specific panel-of-

normals (PoN) and a WES-specific PoN were generated. (B) Distinction between pipelines and

their associated methodologies for SD01 and TCGA samples, and the difference between RNA-

seq and WES pipeline used in this study.

Figure 2. Read count, filtering by MuTect2, mutation spectrum and total variant count in

GBM samples.

(A) Proportion of reads (using samtools on BAM files) before variant calling. (B) Mutation

spectrum indicating the type of base substitution in total RNA-seq and WES data. The Y-axis

shows the proportion of mutations. (C) MuTect2 filtering statistics. Proportion of variants failing

each MuTect2 filter. PASS stands for the proportion of variants accepted as true and somatic by

MuTect2. The other filters are described in Materials and Methods. (D) Total number of variants

for TCGA samples (average over 9 samples – TCGAav). ALL is the number of variants before

MuTect2 filtering. PASS are the ones accepted as true and somatic by MuTect2. The number scale

on top refers to the top two classes of variants (ALL and PASS). Coding refers to variants from

coding regions. Del stands for variants in coding regions inducing an AA change (non-

synonymous SNVs, frameshift indels or stop gain/loss). The number scale on bottom refers to the

bottom two classes of variants (Coding and Del). The actual number of variants in PASS TCGAav

from RNA-seq (blue), intersection (red) and WES (beige) is also indicated.



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Figure 3. Variant features including allele fraction, coverage, genomic location and

COSMIC/dbSNP content in TCGA samples.

(A) Scatter plot representing the fraction of the altered allele estimated from altered read fraction

(allele fraction) versus coverage at the variant position (total number of reads). Merged data from

9 TCGA samples is shown. (B) Histograms of the distribution of allele fraction (AF) for the

indicated classes of variants. Merged data from 9 TCGA samples is shown. (C) Genomic location

of PASS variants, given as the average value over 9 TCGA samples. (D) Type of variants from

coding regions, given as the average obtained over 9 TCGA samples. Indel stands for insertions

and deletions. (E) Proportion of variants from coding regions included in COSMIC and/or dbSNP,

given as the average obtained over 9 TCGA samples. Absolute numbers of COSMIC-only variants

are indicated.

Figure 4. Heatmap of the 63 most widely mutated genes across TCGA samples in RNA-seq-

only.

Only genes altered in coding regions in at least 5 out of 9 tumor samples are shown. Rows indicate

genes and columns are tumors. Black indicates no variants, bright yellow only one variant/gene

and red 7 variants/gene, with a gradient from yellow to red indicating a number of variants/gene

included between 1 and 7. The heatmap was clustered by rows and columns (the dendrogram is

not shown).

Figure 5. SIFT and FATHMM scores for TCGA samples.

(A) Scatterplots of FATHMM scores versus SIFT scores for non-synonymous SNVs. The

threshold for deleterious variants for SIFT is 0.05 and the threshold for cancer-drivers for

FATHMM is -0.75, which are indicated. Mutations retained by both are thus in the bottom left

corner of each plot. Pearson correlations between SIFT and FATHMM are indicated by R values



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for each plot. (B) Box plots showing the distribution of FATHMM scores for each of the indicated

categories of variants. The only significant difference was between the Intersection and WES-only

groups (p = 0.0085). Genes with the smallest variant scores are indicated. (C) Box plots showing

the distribution of SIFT scores. No significant differences were observed. (D) Proportion of

variants retained as deleterious or cancer-driver by SIFT and/or FATHMM respectively. The

proportion retained by FATHMM is indicated in red, and the proportion retained by both SIFT

and FATHMM is indicated in blue.

SUPPLEMENTARY FIGURE LEGENDS

Supplementary Figure 1. Uniquely mapped read count.

The number of reads accepted as uniquely mapped reads by STAR aligner is given for each sample,

on the left for WES data and on the right for RNA-seq data. Counts are based on flag-stats given

by samtools, done on BAM files before variant calling.

Supplementary Figure 2. TLOD scores distribution in the SD01 sample.

Histogram showing the distribution of TLOD scores of variants given by MuTect2 separated

between different groups (COSMIC, dbSNP, not COSMIC/ not dbSNP). The Y-axis shows the

proportion of mutations.

Supplementary Figure 3. Proportion of COSMIC and dbSNP variants in coding regions, in

the SD01 sample.

The Y-axis shows the proportion of variants indicated.

Supplementary Figure 4. Chromosomal location of MuTect2 PASS variants in SD01.

The Y-axis shows the proportion of mutations by chromosome.



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SD01 TCGA samples (9 samples)RNA-seq • No Blood-derived Normal

• 3 RNA-seq samples in the PoN• Alignment with STAR 2-pass• Variant Calling with MuTect2• RefSeq annotations (ANNOVAR)• SIFT/FATHMM scoring

• WES Blood-derived Normal• 12 RNA-seq samples in the PoN• Alignment with STAR 2-pass• Variant Calling with MuTect2• RefSeq annotations (ANNOVAR)• SIFT/FATHMM scoring

WES • No Blood-derived Normal• 3 WES samples in the PoN• Alignment with STAR 1-pass• Variant Calling with MuTect2• RefSeq annotations (ANNOVAR)• SIFT/FATHMM scoring

• WES Blood-derived Normal• 12 WES samples in the PoN• Alignment with BWA• Variant Calling with MuTect2• RefSeq annotations (ANNOVAR)• SIFT/FATHMM scoring

Other Normal Samples

Variant Calling Normal vs Tumor MuTect2

Alignment STAR 2-pass, bwa

Variant Calling Artifact Detection Mode MuTect2

PoN Compilation

Variant list in VCF format

PoN VCF

Blood-derivedMatched-Normal

Tumor Sample

Processing raw reads : trimming the adapters, removing r/tRNA contaminants

Processing BAM files : Mark duplicates, split N Cigar, Base recalibration

SD01 9 TCGA samplesRNAseq • No Blood-derived Normal

• 3 RNAseq samples in the PoN • Alignment with STAR 2pass • Variant Calling with MuTect2 • Annotations with ANNOVAR • scoring with FATHMMcw/SIFT

• WXS Blood-derived Normal • 12 RNAseq samples in the PoN • Alignment with STAR 2pass • Variant Calling with MuTect2 • Annotations with ANNOVAR • scoring with FATHMMcw/SIFT

WXS • No Blood-derived Normal • 3 WXS samples in the PoN • Alignment with STAR 1pass • Variant Calling with MuTect2 • Annotations with ANNOVAR • scoring with FATHMMcw/SIFT

• WXS Blood-derived Normal • 12 WXS samples in the PoN • Alignment with BWA • Variant Calling with MuTect2 • Annotations with ANNOVAR • scoring with FATHMMcw/SIFT

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100

200

300

400

500

600

700

800

900

1000

1100

Nu

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Intersection

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1305 | 16 | 244

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Number of variants

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Number of variants

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Figure 2

https://doi.org/10.1101/249219


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xiv a license to display the preprint in perpetuity. It is made available under

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hich was not

this version posted January 17, 2018. ;

https://doi.org/10.1101/249219doi:

bioRxiv preprint

Figure 3

https://doi.org/10.1101/249219


>12 1 0

GBM08

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TPT1PSAP

HNRNPA3RNF19A

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Figure 4

https://doi.org/10.1101/249219


0

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Figure 5

https://doi.org/10.1101/249219


0 50 100 150

SD01GBM01GBM02GBM03GBM04GBM05GBM06GBM07GBM08GBM10

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0 50 100 150

RNA-seq

Uniquely mapped reads

x 106 x 106



Supplementary Figure 1

https://doi.org/10.1101/249219


0 25 50 100 150 2000

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TLOD scores

Dens

ity

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0 25 50 100 150 200TLOD scores

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COSMICdbSNPnot COSMIC/not dbSNP




https://doi.org/10.1101/249219


WES

only

Intersection

RNA-seqonly0

0.10.20.30.40.50.60.70.80.91 COSMIC only

COSMIC and dbSNPdbSNP onlynot COSMIC not dbSNP




https://doi.org/10.1101/249219


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y

0

0.02

0.04

0.06

0.08

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Chromosome

Proportion

RNA-seq onlyWES-only




https://doi.org/10.1101/249219


Detection and benchmarking of somatic mutations in cancer ...€¦ · 2018-01-17 · in WES. We...

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